Download Effects of the Exchange Rate on Output and Price Level:Evidence

The Lahore Journal of Economics
12 : 1 (Summer 2007) pp. 49-77
Effects of the Exchange Rate on Output and Price Level:
Evidence from the Pakistani Economy
Munir A. S. Choudhary* and Muhammad Aslam Chaudhry**
Abstract
The question of whether devaluation of the currency affects output
positively or negatively has received considerable attention both from
academic and empirical researchers. A number of empirical studies have
supported the contractionary devaluation hypothesis using pooled time
series data from a large number of heterogeneous countries. Since the
effects of devaluation on output and the price level may not be uniform
across all developing countries, the empirical results can not be
generalized for all countries. In addition, almost none of the empirical
studies used to test the contractionary devaluation hypothesis separate the
effects of devaluation from import prices. Thus, a country specific study is
needed that separate the effects of devaluation from the import price
effects. This paper uses a VEC model to analyze the effects of the exchange
rate on output and the price level in Pakistan for the period 1975-2005.
Our analysis shows that devaluation has a positive effect on output but a
negative effect on the price level. Thus, the evidence presented in this
paper does not support the contractionary devaluation hypothesis for the
Pakistani economy.
I. Introduction
There are numerous channels through which the effects of currency
fluctuations are transmitted onto the domestic price level and output. Under
a fixed exchange rate system, official changes in the value of a country’s
currency relative to other currencies are called devaluations and
revaluations. Whereas under a flexible exchange rate system, market forcegenerated changes in the value of the country’s currency are known as
depreciations and appreciations. In this paper, the terms depreciation and
devaluation are used interchangeably.
*
Foreign Professor, Department of Economics, University of the Punjab
Professor and Chairman, Department of Economics, University of the Punjab
**
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
50
According to the conventional textbook model, depreciation of the
domestic currency makes the country’s exports relatively cheaper for
foreigners and makes foreign goods relatively more expensive for domestic
consumers. This helps to increase the countries exports and switches
demand towards domestically produced goods, and therefore shifts the
aggregate demand curve to the right (Dornbusch 1988). In the short-run,
when the economy is operating at a positively sloped aggregate supply
curve, a depreciation of the domestic currency will cause both output and
the price level to increase. However, in the long-run when the economy is
operating on the vertical portion of the aggregate supply curve, the price
level will increase proportionately with no effect on the output level. Under
the assumption that devaluations are expansionary, Pakistan like many other
developing countries, resorted to large devaluations in the hope of reaping
economic benefits1. During the fixed exchange rate period (1971-81), the
Pak rupee was devalued from 4.79 to 9.90 per US dollar. During the
managed float period (1982-1999) the rupee was devalued from 9.90 to
51.78 per US dollar. During the flexible exchange rate period (2000-2006)
the rupee has depreciated from 51.78 to 60.6 per US dollar. The nominal
effective exchange rate, which measures the value of the Pak rupee against a
weighted average of foreign currencies, shows a similar trend. As shown in
figure 1, the nominal effective exchange rate (2000=1000) has continually
declined since 1982.
Figure 1
350
300
250
200
150
100
50
0
1975
1980
1985
1990
1995
2000
2005
Nominal effective exchange rate
Consumer Price Index - Seasonally adjusted
Manufacturing output Seasonally adjusted
As mentioned above, the conventional textbook view predicts that
the price level and output are negatively related to the value of the
domestic currency. To illustrate this negative correlation, the consumer
Effects of the Exchange Rate on Output and Price Level
51
price index and manufacturing output index are plotted in figure 1 and
their correlation coefficients are given below in Table-1.
Table-1: Correlation Coefficients between Exchange Rate – Price Level
and Output
Variables
NEER-CPI
NEER-Output
Period
Correlation Coefficient
1975:1– 2005:4
1975:1 – 2005:4
-0.80
-0.75
Note: CPI denotes consumer price index seasonally adjusted, Output is
manufacturing production, NEER is nominal effective exchange rate.
The correlation coefficients are all different from zero at the 5%
significance level.
As shown in Table-1, the correlation between the exchange rate and
price level is -0.80; between exchange rate and output, it is -0.75. These
negative and statistically significant coefficients indicate that devaluations
have an expansionary effect on the Pakistan economy as predicted by the
conventional view. Yet, one should not read too much from the correlation
analysis. This strong negative correlation between the exchange rate and
other variables as shown in figure 1 and in Table-1 may be a spurious
correlation. Furthermore, the bivariate results do not provide information
regarding the channels through which the exchange rate might affect
output.
However, this textbook view is not uniformly supported either by
prior theoretical research or actual historical experience. In recent years, a
growing literature argues for contractionary devaluation by concentrating on
aggregate demand and aggregate supply models. The most important
channels through which devaluation may create negative effects on
aggregate demand and thus contraction in output are:
Income Redistribution Channel: Devaluation can generate a
redistribution of a given level of real income from wages to profits (DiazAlejandro, 1963; Cooper, 1971a; Knight, 1976; and Krugman and Taylor,
1978). The effect of income redistribution is based on the argument that
there are different consumer groups in a society. These groups can be
broadly divided into two categories: wage earners and profit earners. The
marginal propensity to save is assumed to be higher for profit recipients
than for wage earners. Devaluation leads to higher prices and profits in
export and import competing industries while sticky nominal wages reduce
52
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
real wages. Since the marginal propensity to save is higher from profits than
from wages, the economy’s average propensity to save will rise and this will
tend to be demand contractionary.
Interest Rate Channel: Devaluation leads to higher interest rates
(Bruno, 1979, and Van Wijnbergen, 1986). An increase in the domestic
price level as a result of devaluation will increase the demand for nominal
money (which is same thing as a decrease in real money balances) and thus
the nominal interest rates. The increase in the interest rate will tend to
reduce investment and consumption expenditure through traditional
mechanisms.
Investment Channel: A decline in new investment (Branson, 1986;
Buffie, 1986 and Van Wijnbergen, 1986). Since a substantial portion of any
new investment in developing countries consists of imported capital goods,
depreciation raises the cost of imported capital and reduces its imports. This
discourages new investment and exerts contractionary pressure on aggregate
demand.
External Debt Channel: Devaluation results in a higher cost of large
external debts (Cooper, 1971b; Gylfason and Risager, 1984 and Van
Wijnbergen, 1986). In developing countries, most external debt is
denominated in dollars or in another strong foreign currency. If a country
having large debt devalues its currency, then both residents and the
government need more domestic currency to pay for the same amount of
foreign debt. This reduces the net wealth and therefore aggregate
expenditure. Another argument is that in developing countries, a very large
proportion of the external debt is owed by the public sector. Devaluation
increases the domestic currency costs of serving debt. The government can
finance increased debt service payments by reducing its expenditures,
increased taxation or domestic borrowing. All these modes of financing have
contrationary effects on aggregate demand.
Real Balance Channel: Devaluation causes a decrease in real balances
(Bruno, 1979; Gylfason and Radetzki, 1991). Devaluation increases the
prices of traded goods and that leads to an increase in the general price
level. An increase in the general price level decreases real cash balances and
real wealth which tends to decrease personal spending.
Redistribution of income from the private sector to the government
sector (Krugman and Taylor, 1978). Since the demand for imported goods
is inelastic, devaluation increases the domestic currency value of imports
while their volume remains unchanged. This increased domestic currency
Effects of the Exchange Rate on Output and Price Level
53
value of trade causes ad valorem trade taxes (tariff revenue) to rise. As a
result, there will be a redistribution of income from the private sector to
the government. An increase in government’s tax revenue means less is left
for the private sector. In the short-run, the marginal propensity to save for
the government sector is close to one, thus government spending remains
unchanged and aggregate demand decreases due to decrease in private
consumption.
Similarly, a number of supply-side channels are identified through
which devaluation can be contractionary. Some of these supply-side channels
are discussed below:
Higher cost of imported inputs channel (Krugman and Taylor,
1978; Bruno, 1979; Gylfason and Schmid, 1983; Hanson, 1983; Gylfason
and Risager, 1984; Solimano, 1986; Edwards, 1986; VanWijnbergen, 1986
and Gylfason and Radetzki, 1991). In many developing countries imports
consist of predominately noncompetitive intermediate inputs or capital
goods. Devaluation increases the domestic currency cost of imported
inputs and reduces the volume of imported inputs. A reduction in imports
implies insufficient inputs necessary for production. Thus, because of the
lack of inputs and higher cost relative to the prices of their domestic final
products, firms tend to produce less, which leads to a reduction in
aggregate supply.
Higher wage costs channel (Krugman and Taylor, 1978; Bruno,
1979; Gylfason and Schmid, 1983; Hanson, 1983; Gylfason and Risager,
1984; Solimano, 1986; Edwards, 1986; VanWijnbergen, 1986 and Gylfason
and Radetzki, 1991). Increased prices of traded goods caused by devaluation
ultimately result in a general price level increase. As real wages decrease,
the workers will demand higher nominal wages to protect their purchasing
power. If wages are flexible or there exists a wage indexation mechanism,
the nominal wages will adjust proportionately to the general price level.
Such increases in wages increase the cost of production and could produce
adverse supply effects.
Higher cost of working capital channel (VanWijnbergen, 1986).
Working capital is basically short-term funds needed by the firms to carry
out their daily business. Devaluation increases the price level hence the real
money supply decreases. This leads to a decline in the real volume of credit.
If the credit supply decreases relative to demand, interest rates tend to
climb, making working capital more costly. This will push up the cost of
production, hence adversely affecting the supply of output.
54
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
Foreign exporters as price taker in the domestic economy (knitter,
1989). Foreign exporters face an upward sloping marginal cost curve and a
horizontal marginal revenue curve. The quantity produced and supplied by
the foreign producer is determined by the intersection of marginal revenue
and marginal cost curves. Now suppose that the value of the domestic
currency decreases. The price received by a foreign producer in terms of his
own currency will be lower. In other words the marginal revenue curve
faced by foreign producer will shift down and it will intersect the marginal
cost curve at a lower level of output. At a given price level in the domestic
economy, the output supplied by the foreign producer will decrease and this
will result in a leftward shift of aggregate supply curve in the domestic
economy.
In spite of the renewed theoretical interest in the possible
contractionary effects of devaluations, the empirical evidence is mix at best.
A review of existing studies indicates that four major empirical approaches
have been utilized to investigate the effects of devaluation on output: The
control group approach, the before and after approach, the macrosimulation approach and the econometric approach.
The control group approach (Donovan, 1982; Gylfason, 1987;
Kamin, 1988; Edwards, 1989 and Khan, 1990). This approach aims at
separating the effect of devaluation from other factors on output. Donovan
(1982) studied 78 IMF-supported devaluations. He concluded that economic
growth fell by more than the average decline experienced by non-oil
developing countries in one-year comparisons but by less in three years
comparisons. Gylfason (1987) studied 32 IMF-supported programs during
1977-1979 and found that differences in output growth between countries
with IMF programs and non-program countries were not statistically
significant. Kamin (1988) analyzed 107 devaluations between 1953 and 1983
and concluded that devaluation has either an expansionary or no effect.
Contraction takes place prior to devaluation and continues after devaluation.
Edwards (1989) studied 18 devaluations in Latin America and concluded that
declines in output growth were not due to devaluation, but due instead to
the accompanying restrictions that have accompanied devaluations. Khan
(1990) studied the effects of IMF supported programs in a group of 69
developing countries over 1973-1988. He found that devaluations were
contractionary but this result was not statistically significant.
The before and after approach (Diaz-Alejendro, 1965; Cooper,
1971a; Killick, Malik, and Manuel, 1992). This approach studies changes in
country performance at the time of devaluation. Diaz-Alejendro (1965)
examined the experience of Argentina over the period 1955-1961. He
Effects of the Exchange Rate on Output and Price Level
55
concluded that the 1959 devaluation of the peso was contractionary as it
shifted the income distribution toward high savers, which depressed
consumption. Cooper (1971a) analyzed 24 devaluations that took place
between 1953 and 1966 in less developed countries. After looking at the
behavior of the major components of aggregate demand, he concluded that
devaluations had a contractionary effect on output. Killick, Malik, and
Manuel (1992) examined the results of 266 IMF-supported programs
implemented during the 1980s. Many of them incorporated nominal
devaluation as a key policy measure. They concluded that these programs
had no noticeable effect on output growth in short term; however, over the
longer term growth rates were improved.
The macro-simulation approach (Gylfason and Schmid, 1983;
Gylfason and Risager, 1984; Solimano, 1986; and Roca and Priale, 1987).
This approach uses simulation models to analyze the impact of exchange
rate changes on output. Gylfason and Schmidt (1983) constructed a small
macro model of an open economy. They found that a devaluation causes
expansionary effects through aggregate demand and contractionary effects
through aggregate supply. Their study concluded that devaluation was
expansionary in 8 out of 10 countries. Gylfason and Risager (1984) studied
the effects of devaluation for 8 developing and 7 developed countries. They
concluded that devaluation was expansionary in developed countries and
contractionary in developing countries. Solimano (1986) constructed a
macroeconomic model for Chile and concluded that devaluation was
contractionary in the short to medium run. Roca and Priale (1987)
constructed a macroeconomic model for the Peruvian economy and
concluded that devaluations were contractionary.
The econometric approach (Sheehey, 1986; Edwards, 1989;
Morley, 1992; Upadhyaya, 1999 and Bahmani-Oskooee and Miteza, 2006).
This approach applies econometric methods to time series data to
investigate the effect of devaluations on output. Sheehey (1986) used cross
section data from 16 Latin American countries and concluded that
devaluations had a contractionary effect on output. Morley (1992) also
used cross section data from 28 developing countries and found support
for the contractionary devaluation hypothesis. Edwards (1989) used panel
data regressions for 12 developing countries and found that devaluations
were contractionary in the short-run. Upadhyaya (1999) applied
cointegration and error correction modeling techniques to data from 6
Asian countries and concluded that devaluations were contractionary for
Pakistan and Thailand but neutral for India, Sri Lanka, Malaysia and
Philippines in the long-run. Bahmani-Oskooee and Miteza (2006) applied
panel unit root and panel cointegration techniques to annual data from 42
56
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
countries and concluded that in the long-run devaluations were
contrationary in non-OECD countries.
In addition to cross-section econometric studies, a number of
econometric studies have also used time series data to investigate the
contractionary devaluation hypothesis. Bahmani-Oskooee and Rhee (1997)
using Korean quarterly data over the period 1971-1974 applied Johansen's
cointegration and error-correction technique. Their error-correction model
confirmed that there exists a long-run relationship between output, money
and the real exchange rate variables. They concluded that real
depreciations were expansionary in the long-run and the most important
expansionary impact of real depreciations appeared with a lag of three
quarters. Domaç (1997) examined the contractionary devaluation
hypothesis in Turkey for the period 1960-1990 by distinguishing the
growth effects of anticipated and unanticipated devaluations. He found
that unanticipated devaluations had a positive impact on real economic
activity, while anticipated devaluations did not exert any significant effect
on output. Bahmani-Oskooee (1998) used quarterly data from 23 LDCs
countries over the 1973-1988 periods to investigate the long-run effects of
devaluation. He used ADF tests to check whether output and effective
exchange rate were cointegrated. He concluded that devaluations were
neutral with respect to output in the long-run for most LDCs. However,
when Bahmani-Oskooee et al. (2002) applied Johansen’s cointegration
technique, they found that devaluations were expansionary in the
Philippines and Thailand but contractionary in Indonesia and Malaysia. De
Silva and Zhu, (2004) considered the case of Sri Lanka and applied the
VAR technique. Using quarterly data over the period 1976-1998, they
concluded that devaluation improved the trade balance but had a
contractionary impact on the Sri Lankan economy.
The studies reviewed above show that results concerning the
effects of devaluation on output are quite mixed. This leaves room for new
empirical research. The econometric approach has desirable properties
because it enables the researcher not only to capture the effects of
devaluation but also the effects of other factors on output. Almost all
econometric studies that support the contractionary devaluation hypothesis
use pooled time series data from a large number of heterogeneous
countries. Since the effects of devaluation on output and price level may
not be uniform across all developing countries, an empirical study of the
individual experience of Pakistan can be a valuable addition to the
literature.
Effects of the Exchange Rate on Output and Price Level
57
The objective of this paper is to examine the effects of exchange rate
changes on the price level and real output using data on Pakistan. In section
II, the methodology used in this paper is discussed. Section III describes the
data, estimation and evaluation of empirical results. Section IV gives the
concluding remarks.
II. Methodology
The main objective of this paper is to investigate the effects of
exchange rate fluctuations on inflation and output growth. As is well
known, under a fixed exchange rate system monetary authorities maintain
the exchange rate at a predetermined level and allow the macroeconomic
variables such as the price level and output to fluctuate. Thus, under a
fixed exchange rate system, the exchange rate is treated as an exogenous
variable and causality runs from exchange rate to other macroeconomic
variables. However, in a flexible exchange rate system, the exchange rate
like many other macroeconomic variables, is determined by the market
conditions. Thus, under a flexible exchange rate system, the exchange
rate, price level and output are expected to affect each other. Since this
study covers the flexible exchange rate period, a vector autoregression
(VAR) methodology is appropriate because it allows interaction among
macroeconomic variables. The basic version of VAR is regarded as an
unrestricted reduced form of a structural model. One advantage of this
approach is that the specification is purely determined on the information
contained in the available data and does not need any additional nontestable a priori restrictions.
We assume that the economy is described by a system of six
equations: an import price level equation, an exchange rate equation, an
interest rate equation, a money supply equation, a price level equation and
an output level equation. We set up the following VAR model with the
vector of six endogenous variables2:
Zt
(F t , M t , R t , E t , Pt , Yt )
(1)
where
F = Unit value of imports. The unit value of imports is included in the
VAR to capture the effects of foreign supply shocks on domestic
macroeconomic variables. To separate the effects of the unit value of
imports from the foreign exchange rate effects, the unit value of
imports is measured in U.S. dollars.
58
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
M = M2 definition of money supply.
R = Short-term interest rate measured by call money rates.
E = Nominal effective exchange rate. Although many studies have used
bilateral exchange rates in their analysis, Mohsen Bahmani-Oskooee
and Iler Mteza (2006) have pointed out that a country’s currency
could depreciate against one country and appreciate against an
other country and thus the effective exchange rate is the
appropriate concept to capture variation in the overall value of
the currency.
P = Domestic price level measured by consumer price index.
Y = Real output. Since quarterly data on real output is not available for
Pakistani economy, manufacturing output is used as a proxy for real
output.
We assume that the dynamic behavior of Zt is governed by the
following structural model:
B(L)'Z t
Pt
(2)
where B(L) is a kth order matrix polynomial in the lag operator L such that
B(L) = B0 – B1L – B2L2 – …….. – BkLk. B0 is a non-singular matrix normalized
to have one on the diagonal and summarizes the contemporaneous
relationship between the variables contained in the vector Zt. Pt is a vector
of structural disturbances and is serially uncorrelated. E(PtPt) = 6P and it is
a diagonal matrix while E represents the expectations sign. 6P is a diagonal
matrix where diagonal elements are the variance of the structural
disturbances and off-diagonal elements are zero (structural error terms are
assumed to be mutually uncorrelated).
The empirical estimation of (2) is achieved by applying the Wold
representation theorem and inverting it to derive the reduced form VAR
which is given below:
A(L) 'Z t
Ht
(3)
Effects of the Exchange Rate on Output and Price Level
59
where A(L) is matrix polynomial in the lag operator L, H t is a vector of
'
serially uncorrelated reduced form disturbances and E(H t H t ) ¦ H . The
components of equation (3) to equation (2) are related as given below:
A(L)
-1
2
I - A 1L A2 L .......... .. Ak L
B 0 B (L )
-1
Ht
B0 Pt
k
(4)
(5)
In order to recover the structural parameters of the VAR model
specified by equation (2) from the estimated reduced form coefficients of
equation (3), the model must be either exactly identified or over identified.
Exact identification requires that number of parameters in B0 and 6P are
equal to number of parameters in the covariance matrix 6t. Using equations
(4) and (5), the parameters of structural equation (2) and of reduced form
equation (2) are related as below:
-1
2
k
A(L)
I - B0 [B1L B 2 L .......... . B k L ]
¦H
B0 ¦ P B0
-1
-1
(6)
(7)
Estimates of B0 and 6P can be obtained only through sample
estimates of 6t. Given that the diagonal elements of B0 are all unity, B0
contains (n2-n) unknown parameters. 6P contains n unknown values. Thus,
the right hand side of equation (7) has a total of n2 unknown values. Since
6t is symmetric, it contains only (n2+n)/2 distinct known elements. In order
to identify the n2 unknown structural parameters from the known (n2+n)/2
distinct elements of 6t, the minimum requirement is to impose (n2-n)/2
restrictions on the system. In the structural VAR model, B0 can be any
structure as long as it satisfies the minimum restriction requirement.
There are several ways of specifying the restrictions to achieve
identification of the structural parameters. A simple method is to
orthogonalize reduced form errors by Choleski decomposition as originally
applied by Sims (1980). This is fairly popular method because it is easy to
handle econometrically. This approach to identification requires the
assumption that the system of equations follows a recursive scheme.
However, this approach should only be used when the recursive ordering
implied by this identification is supported by theoretical consideration. The
60
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
alternative and more common approach to identify a structural VAR is to
use the restrictions that are implied from a fully specified macroeconomic
model. The structural VAR model estimated by Blanchard and Watson
(1986), who uses theory to impose short-run restrictions, is an example of
this approach. Since the Choleski decomposition is a special case of a more
general approach used by Blanchard and Watson and it is easy to handle
econometrically, we use this approach to identify the restrictions. The
relationship between the reduced form VAR residuals and the structural
innovations is given below in equation (8):
ªH f º
ªf 0 º
ª1
« »
«
»
«a
m
«H m »
« 0»
« 21
«H r »
«r »
«a
« » = « 0 » + « 31
«H e »
«e 0 »
«a 41
«H »
«p 0 »
«a 51
« p»
« »
«
y 0 ¼»
«H y »
«
¬
¬«a 61
¬ ¼
0
1
a 32
a 42
a 52
a 62
0
0
1
a 43
a 53
a 63
0
0
0
1
a 54
a 64
0
0
0
0
1
a 65
0º
0»
»
0»
»
0»
0»
»
1 ¼»
ªP f º
« »
« Pm »
« Pr »
« »
«Pe »
«P »
« p»
«P y »
¬ ¼
(8)
where f0,e0,r0,m0,p0 and y0 are constants and aij represent coefficients. As
shown in equation (8), the import price level equation is ordered first
because the reduced form residuals in this equation are unlikely to be
contemporaneously affected by any other shocks other than their own. This
restriction implies that import prices do not respond to contemporaneous
changes from other variables but all other variable in the system are
contemporaneously affected by change in import prices in a small open
economy like Pakistan. The money supply equation is ordered next because
it reasonable to assume that monetary shocks have contemporaneous effects
on all domestic variables in the system. The nominal interest rate equation
is ordered third because in Pakistan, the interest rate is effectively managed
by the State Bank of Pakistan at a predetermined level and does not respond
to contemporaneous changes in other macro economic variables except
import prices as it puts pressure on the country’s limited foreign reserves.
The nominal effective exchange rate equation is ordered next because
import prices and monetary shocks have a contemporaneous effect on the
exchange rate. The price level equation is ordered fifth because
contemporaneous shocks in all nominal variables in the system are likely to
affect the residuals in the price level equation. The output equation is
ordered last by assuming that output is contemporaneously affected by all
shocks in the system.
Effects of the Exchange Rate on Output and Price Level
61
To avoid spurious statistical inferences, the VAR models are usually
estimated in first difference form if the data series are non-stationary in the
level form. Shocks to the differenced variables will have a temporary effect
on the growth rate but a permanent effect on its level. Estimation of a VAR
model with stationary variables is consistent regardless whether the time
series are cointegrated or not. If, however, the series are integrated of order
one, I(1), and cointegrated, then we need to include additional information
gained from the long-run relationship to get efficient estimates. This
requires the inclusion of a vector of cointegrating residuals in the VAR with
differenced variables. This is known as a vector error correction model
(VECM).
III. Data and Estimation Results
Data
The key macroeconomic variables are manufacturing output index
(Yt), consumer price index (Pt), M2 definition of money supply (Mt),
nominal effective exchange rate (Et), short-term interest rate measured by
call money rates (Rt) and unit value of imports (Et). The unit value of
imports index (2000=100) is measured in U.S. dollars by using the
bilateral nominal exchange rate of Pakistan rupee and U.S. dollar. The
nominal effective exchange rate index (2000=100) is a weighted average of
major trading partners and an increase in index means appreciation. The
data are quarterly and the sample period for the variables is 1975:12005:4. All variables are in nominal values, except manufacturing output.
In addition, all variables are taken from International Financial Statistics
(IFS) and measured in logarithmic form. Manufacturing output, consumer
price index, unit value of imports and money supply series are seasonally
adjusted using X12 program.
Unit root tests
We first test the hypothesis that a time series contains a unit root
and thus follows a random walk process. The implication of this test is to
determine whether the VAR model should be estimated in the level or first
difference form. To test for unit roots, we use the augmented Dicky-Fuller
(ADF) test and and the Philips-Perron test. Test results for unit roots are
reported in Table-2.
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
62
Table-2: ADF and PP tests for unit roots
Variable
Ft
ADF test for a unit root
Without trend With trend
-0.80
-3.20
Pp test for a unit root
Without trend With trend
-0.70
-2.19
Et
-0.03
-2.53
-0.07
-2.53
Rt
-2.78
-2.94
-3.76**
-4.04**
Yt
0.18
-1.19
-0.34
-2.02
Pt
-0.88
-1.34
-1.14
-1.30
Mt
-0.65
-2.60
-1.40
-2.93
'Ft
-5.77**
-5.75**
-11.95**
-11.92**
'Et
-10.21**
-10.41**
-10.21**
-10.39**
'Rt
-5.05**
-5.02**
-15.59**
-15.52**
'Yt
-11.40**
-11.37**
-16.60**
-16.53**
'Pt
-5.08**
-5.12**
-9.04**
-9.04**
-3.71**
-3.56*
-10.76**
'Mt
Critical Values for rejecting the null hypothesis of unit root
ADF test
PP test
Without drift
With drift
Without drift
1%
-3.49
-4.04
-3.48
5%
-2.89
-3.45
-2.88
10%
-2.58
-3.15
-2.58
-10.87**
With drift
-4.03
-3.45
-3.15
Note: unit root tests are performed for the period 1975:1-2005:4
ADF test:
'X t
I 0 I1 X t - 1 k
¦ D j 'X t - j Kt
j 1
PP test:
Xt
E 0 E 1X t - 1 H t
where Xt represent the natural log of a time series in the level form. ' is
the first difference operator. The tabulated values are t-statistics to test the
null hypothesis of unit root (M1 = 0) for ADF test and (B1 = 0) for PP test.
The appropriate lag length (k) was selected using Akike information criterion
with maximum lags(k)=8. *indicates significant at 5% level. **indicates
significant at 1% level.
Effects of the Exchange Rate on Output and Price Level
63
As shown in Table-2, the ADF and PP tests are reported with and
without the time trends. Examination of the test results in table 2 shows
that the ADF test fails to reject the null hypothesis of single unit root for all
variables. However, the PP test strongly rejects the null hypothesis of single
unit root for the interest rate variable. Since the ADF and PP tests provide
conflicting results for the interest rate series, we also performed the
Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) (1992) test to check for
unit roots in the interest rate series. The KPSS test differs from the other
unit root tests because it reverses the null (unit root) and the alternative
(stationary) hypotheses. The KPSS test strongly rejected the null hypothesis
of stationary for interest rate series.
The null hypothesis of two unit roots is rejected by both tests for all
variables at the 5% significance level. Thus, the evidence suggests that first
differencing is sufficient for modeling the time series considered in our
study.
Tests for cointegration
Unit root tests reveal that variables included in the VAR model are
I(1). Next, we perform Johansen’s cointegration test (Johansen, 1991) to see
whether these variables are cointegrated. To determine the number of
cointegrating vectors, Johansen developed two likelihood ratio statistics:
Trace statistics (Otrace) and maximum eigenvalue statistic (Omax). The results of
cointegration tests are reported in Table-3.
Table-3: Johansen Cointegration tests
K=2
Trace test (Otrace)
Maximum eigenvalue test (Omax) k=2
H0
HA
H0
HA
Critical
(Otrace) Critical
(Omax)
Values 5%
Values 5%
r = 0 r = 0 44.45
40.07
r d 0 r > 0 108.01 95.75
rd1
r>1
63.56
69.82
r=1
r=1
21.30
33.88
rd2
r>2
42.26
47.86
r=2
r=2
19.23
27.58
rd3
r>3
23.03
29.80
r=3
r=3
15.74
21.13
rd4
r>4
7.28
15.49
r=4
r=4
6.98
14.26
rd5
r>5
0.30
3.84
r=5
r=5
0.30
3.84
Note: r represents number of cointegrating vectors and k represents the
number of lags in the unrestricted VAR model.
64
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
Cointegration tests are performed under the assumption of a linear
trend in the data, and an intercept but no trend in the cointegrating
equation. With maximum lags set to eight, the lag length was selected using
different lag selection criteria in the unrestricted VAR model. Sequential
modified likelyhood ratio test, final prediction error criterion and Akaike’s
information criterion all selected two lags in the unrestricted VAR model. As
shown in table 3, the null hypothesis that the variables in the VAR are not
cointegrated (r=0) is rejected at the 5% significance level under both tests.
However, the null hypothesis of one cointegrating relation among the
variables (r=1) can not be rejected under either test.
Reduced Form Estimation Results
Having established that all variables in the model are I(1) and
cointegrated, a VECM with one cointegrating relation and two lags in each
equation was estimated. The VECM allows the long-run behavior of the
endogenous variables to converge to their long-run equilibrium relationship
while allowing a wide range of short-run dynamics. To save space, only the
estimated coefficients of the cointegrating equation and other statistics in
VECM are reported in table 4 below.
Table-4: Summary of VECM estimation
Error Correction
Term
Adjusted R 2
Term
Import Money Interest Exchange Price Output
Prices Supply
Rate
Rate
level
(F)
(M)
(R)
(E)
(P)
(Y)
-0.046
0.007 -0.016
-0.019 0.001 -0.039
(4.27)
(1.78)
(0.32)
(1.43) (0.51) (4.35)
0.10
0.05
0.14
0.02
0.11
0.25
-0.02
0.06
0.27
0.01
0.31
0.04
Note: The VECM was estimated using two lags in each equation. Absolute
t-values are given in parentheses. SEE stands for the standard error
of the equation.
Examination of Table-4 shows that the long-run relationship is
established at the 1% significance level for output and import prices and at
the 10% level for money supply within two quarters. However, for other
variables convergence to their equilibrium path takes longer than two
quarters.
Effects of the Exchange Rate on Output and Price Level
65
Granger Causality Tests
The VECM approach not only enables us to determine the direction
of causality among the variables, but it also allows us to distinguish between
the two types of Granger causality: short-run and long-run causality. The
long-run causality from independent variables to the dependent variable is
evaluated by testing the null hypothesis that the coefficient ( O ) of the error
correction term (ECt-1) is zero. Short-run causality from an independent
variable to the dependent variable is evaluated by testing the null hypothesis
that each coefficient (Ei) on the independent variable is zero. By rejecting
either of the two hypotheses, we conclude that independent variables
Granger cause the dependent variable. Based on our VECM, Granger
causality tests are reported below in Table-5.
Table-5: Results of Granger causality tests
Independent variables
ECt-1
'Ft
'Mt
'Rt
t( = 0)
'Et
'Pt
'Yt
F(Ei = 0)
Dependent variable
'Fi
-4.27*** - - - - 1.69
'Mi
1.78*
'Ri
0.64
0.14
2.32* 0.56
1.14
- - - - 0.87
0.44
2.35* 2.01
-0.32
0.08
1.65
----
0.50
2.12
2.73*
'Ei
-1.43
0.26
0.28
0.67
----
2.19
0.90
'Pi
0.51
3.32** 4.46** 0.35
'Yi
-4.35*** 2.67* 0.62
0.74
7.80*** - - - - 0.70
0.10
1.70
----
Note: t(=0) and F(i=0) are the t-statistic for testing the null hypothesis
that the coefficient of error correction term is zero and the standard
F-statistic for testing the null hypothesis that all coefficients on the
independent variables are zeroes, *** , ** , * indicate significance at
the 1% , 5% and 10%, levels respectively.
Results presented in Table-5 indicate the presence of long-run
causality from all the variables to output growth. However, there is only
short-run causality from imported inflation, money growth and exchange
rate to domestic inflation. In short, we can say that exchange rate does
Granger cause inflation and output growth.
66
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
Variance decomposition results
Table-6 presents the variance decomposition of the variables in the
model. The table shows the percentage of the forecast error variance for
each variable that is attributable to its own shocks and to shocks in the
other variables in the system. The most important conclusions from the
variance decomposition are as follows. First, for all variables except the
output (LY), the predominant source of variation are own shocks. Second,
over the medium-term (first 6 quarters), about 76 percent of variance in
output forecast errors is explained by own shocks and only 24 percent by
shocks in all other variables in the system. Over the long-term (24 quarters),
the situation is reversed where own shocks account only about 25 percent
while shocks in other variables explain 75 percent of the forecast error
variance of output. Over the longer period, the domestic price level is the
predominant source of variation in output. Domestic price shocks explain
about 38% of the forecast error variance of output. The effect of interest
rates, import prices and money shocks to output is about 19%, 10% and 7%
respectively. However, surprisingly the impact of nominal effective exchange
rate on output is quite unimportant. Exchange rate shocks account for only
0.5 to 1.5% of the forecast error variance of output. Third, over the shortterm (two quarters), import price shocks are relatively more important in
explaining variation in the price level than shocks in other variables. Import
price shocks interpret 6.27% while shocks in all other variables combined
explain only 6.23% of the forecast error variance of price level over a time
span of two quarters. However, in the long-run, money and exchange rate
shocks become more important to explain variation of price level. The longterm effect of money, the exchange rate, and import price shocks on price
level variation is about 18%, 9% and 6% respectively. The effect of output
shocks on the price level is statistically insignificant at all time horizons.
Fourth, almost all of the variation in the exchange rate and interest rates is
explained by own shocks and money shocks, while the impact of other
variables on the exchange rate and interest rates is insignificant. Money
shocks explain 13.4% and 11.9% of the forecast error variance of the
exchange rate and interest rates, respectively. We can conclude that effects
of monetary policy are transmitted through both the exchange rate channel
and the interest rate channel. Fifth, the variance of money supply is
explained primarily by its own shocks and by output shocks. In summary,
output and the price level do not explain any variation in the exchange rate
but exchange rate shocks do explain variation in the price level.
Table-6: Variance decomposition from the Vector Error Correction Model
Effects of the Exchange Rate on Output and Price Level
67
Variance Decomposition of LFP:
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.07
96.46
0.12
0.00
0.00
1.03
2.40
4.00
0.09
91.15
0.42
0.41
0.67
1.80
5.55
6.00
0.11
84.91
2.12
1.84
1.38
1.26
8.50
8.00
0.13
78.28
4.47
4.01
1.47
1.14
10.62
10.00
0.15
72.04
6.58
6.28
1.35
1.61
12.13
12.00
0.17
66.73
8.17
8.30
1.17
2.42
13.21
14.00
0.19
62.44
9.27
9.99
1.01
3.30
13.98
16.00
0.20
59.01
10.04
11.37
0.87
4.14
14.56
18.00
0.22
56.26
10.58
12.49
0.77
4.89
15.01
20.00
0.23
54.03
10.98
13.40
0.68
5.53
15.37
22.00
0.24
52.19
11.29
14.16
0.61
6.08
15.67
24.00
0.26
50.66
11.53
14.79
0.55
6.55
15.91
Variance Decomposition of LM2:
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.03
1.42
96.77
0.36
0.30
1.15
0.01
4.00
0.05
0.81
96.42
0.49
0.37
1.18
0.73
6.00
0.06
0.76
95.93
0.36
0.52
1.03
1.40
8.00
0.07
0.85
95.41
0.27
0.62
0.80
2.06
10.00
0.08
1.00
94.75
0.28
0.65
0.63
2.69
12.00
0.09
1.19
93.95
0.39
0.64
0.57
3.26
14.00
0.10
1.38
93.12
0.56
0.60
0.60
3.75
16.00
0.10
1.55
92.30
0.74
0.55
0.69
4.16
18.00
0.11
1.71
91.55
0.92
0.51
0.81
4.50
20.00
0.12
1.85
90.88
1.08
0.47
0.94
4.78
22.00
0.12
1.97
90.29
1.23
0.44
1.06
5.01
24.00
0.13
2.07
89.77
1.36
0.41
1.17
5.21
Variance Decomposition of LR:
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
68
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.30
0.05
1.31
95.27
0.69
0.29
2.39
4.00
0.39
0.17
2.15
93.19
0.51
2.20
1.78
6.00
0.47
0.24
3.82
90.96
0.41
2.98
1.58
8.00
0.54
0.29
5.66
88.95
0.38
3.32
1.41
10.00
0.59
0.30
7.25
87.40
0.35
3.41
1.29
12.00
0.65
0.30
8.51
86.24
0.32
3.41
1.22
14.00
0.69
0.30
9.46
85.38
0.30
3.38
1.17
16.00
0.74
0.30
10.19
84.74
0.28
3.36
1.13
18.00
0.78
0.30
10.76
84.24
0.27
3.33
1.10
20.00
0.82
0.30
11.21
83.85
0.25
3.31
1.07
22.00
0.86
0.30
11.58
83.52
0.24
3.30
1.05
24.00
0.90
0.30
11.90
83.25
0.23
3.28
1.04
Variance Decomposition of LNEER:
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.10
0.87
2.05
0.32
95.86
0.89
0.01
4.00
0.13
0.69
4.35
0.22
93.54
0.56
0.62
6.00
0.17
0.57
6.76
0.27
90.99
0.64
0.77
8.00
0.19
0.49
8.65
0.42
88.66
0.88
0.90
10.00
0.22
0.42
10.03
0.58
86.82
1.16
0.99
12.00
0.24
0.37
11.01
0.73
85.41
1.42
1.06
14.00
0.26
0.33
11.70
0.85
84.35
1.66
1.11
16.00
0.28
0.30
12.22
0.94
83.53
1.85
1.15
18.00
0.29
0.28
12.61
1.02
82.89
2.01
1.19
20.00
0.31
0.26
12.92
1.09
82.37
2.15
1.22
22.00
0.32
0.24
13.17
1.14
81.94
2.26
1.24
24.00
0.34
0.23
13.38
1.19
81.58
2.36
1.26
Effects of the Exchange Rate on Output and Price Level
69
Variance Decomposition of LP:
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.02
6.27
3.12
2.78
0.22
87.50
0.11
4.00
0.03
6.47
7.41
3.85
5.42
76.82
0.04
6.00
0.04
6.41
11.14
3.75
7.45
71.22
0.02
8.00
0.05
6.35
13.81
3.66
8.33
67.84
0.02
10.00
0.05
6.31
15.52
3.62
8.75
65.77
0.03
12.00
0.06
6.30
16.55
3.65
8.96
64.50
0.04
14.00
0.07
6.30
17.14
3.70
9.10
63.71
0.05
16.00
0.07
6.31
17.48
3.77
9.19
63.18
0.07
18.00
0.08
6.32
17.68
3.84
9.25
62.83
0.08
20.00
0.09
6.33
17.80
3.91
9.31
62.57
0.09
22.00
0.09
6.34
17.87
3.97
9.35
62.37
0.10
24.00
0.10
6.35
17.92
4.02
9.39
62.22
0.11
Variance Decomposition of LY:
Period
S.E.
LFP
LM2
LR
LNEER
LP
LY
2.00
0.05
6.23
0.03
0.20
0.46
4.08
89.01
4.00
0.06
5.78
0.44
1.32
0.55
6.13
85.77
6.00
0.06
6.96
1.72
3.96
0.43
11.11
75.82
8.00
0.07
7.91
3.35
7.29
0.42
17.02
64.00
10.00
0.08
8.49
4.69
10.29
0.53
22.31
53.70
12.00
0.10
8.90
5.56
12.63
0.69
26.56
45.66
14.00
0.11
9.19
6.08
14.38
0.87
29.83
39.65
16.00
0.12
9.41
6.38
15.69
1.03
32.33
35.16
18.00
0.13
9.56
6.56
16.68
1.18
34.26
31.75
20.00
0.13
9.69
6.68
17.45
1.30
35.78
29.10
22.00
0.14
9.78
6.75
18.07
1.40
36.99
27.01
24.00
0.15
9.86
6.80
18.56
1.49
37.97
25.31
70
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
Impulse Response Functions Analysis
Having shown the dynamic effects of variance decompositions, we
analyze the impulse responses. The variance decompositions do not show
the direction of the dynamic effects of the shocks on the variables in the
system but the impulse responses do. The impulse response function (IRF)
describes the impact of an exogenous shock in one variable on the other
variables of the system. A unit (one standard deviation) increase in the ith
variable innovation (residual) is introduced at date t and then it is returned
to zero thereafter. In general the path followed by the variable Xj,t in
response to a one time change in Xi,t, holding the other variables constant
at all times t, is called the IRF. The traditional impulse response method
based on Cholesky decomposition has been criticized because results are
subject to the orthogonality assumption. If the residuals of two (or more)
equations contained in the VAR system are contemporaneously correlated,
then the impulse responses are not robust to the ordering of the variables.
In fact, the impulse responses may display significantly different patterns
Lutkenpohl (1991). Recently, Pesaran and Shin (1998) have developed a
method called the generalized impulse response function (GIRF) which does
not impose the orthogonality restriction and thus impulse responses are not
sensitive to the ordering of the variables in the VAR and provide more
robust results. In this paper we report results both from traditional IRF and
generalized IRF. Each figure shows the response of a particular variable to a
one time shock in each of the variables included in the VEC model. It
should be noted that a one time shock to the first differenced variable is a
permanent shock to the level of that variable.
Figure 2 contains the impulse-response functions for output (LY).
Examination of the graph shows that the impulse responses meet a priori
expectations in terms of the direction of impact. A positive shock to import
prices has a significant contractionary effect on the output. The effect of a
unit shock to import prices on output occurs immediately and stabilizes
after 15 quarters with output decreasing by approximately one percent of its
baseline level. The effect of a unit shock to money supply on output occurs
after approximately the third quarter, reaching its peak after 12 quarters.
Thereafter the cumulative effects of money supply stabilize with output
increasing by approximately one percent of its baseline level. Positive
interest rate shocks have permanent and negative effects on output. Positive
exchange rate shocks (appreciation) lead to an immediate and long lasting
decrease in output. The impact of the exchange rate appreciation is rather
immediate and long lasting. A unit shock to the exchange rate (appreciation)
causes output to decrease approximately 0.5 percent from its base level.
Positive price level shocks have a very strong negative effect on output. A
Effects of the Exchange Rate on Output and Price Level
71
one unit shock in the price level leads to a 1% decrease in output by the
second quarter and then by more than 2% over a longer time span.
Figure 2
Response to Cholesky One S.D. Innovations
Response to Generalized One S.D. Innovations
R es ponse of Output t o I m port Prices
R esponse of Output to Im port Prices
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
20
22
24
2
4
R esponse of Output to Money
6
8
10
12
14
16
18
20
22
24
22
24
Response of Output to Money
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
20
22
24
2
R esponse of Output to Interest R at e
4
6
8
10
12
14
16
18
20
Response of Output to Interest Rate
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
20
22
24
2
R esponse of Output to Exchange R ate
4
6
8
10
12
14
16
18
20
22
24
Response of Output to Exchange Rate
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
20
22
24
2
R espons e of Output t o Price Lev el
4
6
8
10
12
14
16
18
20
22
24
22
24
R esponse of Output to Pric e Lev el
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
Figure 3 contains the impulse-response functions for the price level
(LP). An examination of figure 3 shows that positive shocks in import prices,
money supply, the interest rate and exchange rate (appreciation) all have a
significant positive and lasting effect on the price level. The effect of output
shocks on the price level is marginally negative in the shorter period but
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
72
unclear over the longer period as it is slightly positive in Cholesky
innovation where as negative in generalized innovations. Over a period of
two quarters, the effect of money supply on the price level is stronger than
the effect of exchange rate appreciation. However over a longer period, the
effect of an exchange rate appreciation on the price level is almost as strong
as the effect of money supply.
Figure 3
Response to Cholesky One S.D. Innovations
Response to Generalized One S.D. Innovations
R espons e of Pric e Lev el to I mport Pric es
R es pons e of Price Lev el to Im port Pric es
.020
.020
.016
.016
.012
.012
.008
.008
.004
.004
.000
.000
-.004
-.004
2
4
6
8
10
12
14
16
18
20
22
24
2
R espons e of Pric e Lev el to Money
4
6
8
10
12
14
16
18
20
22
24
22
24
R espons e of Pric e Lev el to Money
.020
.020
.016
.016
.012
.012
.008
.008
.004
.004
.000
.000
-.004
-.004
2
4
6
8
10
12
14
16
18
20
22
24
2
R esponse of Price Lev el to Int eres t R ate
.020
.016
.016
.012
.012
.008
.008
.004
.004
.000
.000
-.004
-.004
4
6
8
10
12
14
16
18
20
22
24
2
R espons e of Price Lev el t o Ex change R ate
.020
.016
.016
.012
.012
.008
.008
.004
.004
.000
.000
-.004
-.004
4
6
8
10
12
14
16
18
20
22
24
2
R espons e of Pric e Lev el to Output
.020
.016
.016
.012
.012
.008
.008
.004
.004
.000
.000
-.004
-.004
4
6
8
10
12
14
16
18
20
10
12
14
16
18
20
4
6
8
10
12
14
16
18
20
22
24
4
6
8
10
12
14
16
18
20
22
24
22
24
R espons e of Pric e Lev el to Output
.020
2
8
R espons e of Pric e Lev el to Exc hange R ate
.020
2
6
R esponse of Pric e Lev el to Interes t R ate
.020
2
4
22
24
2
4
6
8
10
12
14
16
18
20
Effects of the Exchange Rate on Output and Price Level
73
The effect of a unit shock to money supply on the price level occurs
quickly and reaches its peak within 10 quarters. The cumulative effects of
money supply stabilize with the price level, increasing it by approximately
one percent of its baseline level. The effect of a unit shock to exchange rate
(appreciation) on the price level is rather slow for the first two quarters and
then accelerates quickly and reaches its peak within 10 quarters. The
cumulative effects of the exchange rate stabilize with the price level,
increasing it by approximately 0.8 percent of its baseline level. The positive
effect of the interest rate on the price level confirms the hypothesis that
higher interest rates in the developing countries increase the cost of
working capital (VanWijnbergen, 1986) and thus causes a leftward shift in
the aggregate supply curve.
In summary, a devaluation (negative exchange rate shocks) has a
positive effect on output and a negative effect on the price level. The
negative effect of a devaluation on the price level fits quite well with the
competitive devaluations or “beggar thy neighbor” policies followed by
Pakistan and other developing countries exporting similar products and
desperately attempting to increase their market share in a world of
shrinking markets. Thus, the evidence presented in this paper does not
support the contractionary devaluation hypothesis.
IV. Conclusion
The traditional view was that devaluations are expansionary and that
led Pakistan, like many other developing countries, to resort to large
devaluations in hope to reap economic benefits. During the fixed exchange
rate period (1971-81), the Pak rupee was devalued from 4.79 to 9.90 per US
dollar. During the managed float period (1982-1999) the rupee was devalued
from 9.90 to 51.78 per US dollar. During the flexible exchange rate period
(2000-2006) the rupee has depreciated from 51.78 to 60.60 per US dollar.
In recent years, the proposition that devaluations are expansionary has faced
a serious threat from new structuralists who claim that devaluations are
contractionary. A number of empirical studies have supported the
contractionary devaluation hypothesis using pooled time series data from a
large number of heterogeneous countries. Since the effects of devaluation on
output and price level may not be uniform across all developing countries, it
is desirable to conduct country specific studies.
This study has analyzed the effects of exchange rate on output and
the price level using a VEC model for the Pakistan economy over the period
1975:1-2005:4. Examination of variance decomposition and impulse
responses from a VEC model has revealed a number of important findings.
74
Munir A. S. Choudhary and Muhammad Aslam Chaudhry
First, devaluation has a positive effect on output but a negative effect on the
price level. Thus, evidence presented in this paper does not support the
contractionary devaluation hypothesis. Second, expansionary monetary policy
has a (significant) positive effect on both output and the price level. Third,
an increase in import prices has a negative effect on output but a positive
effect on the price level. Fourth, an increase in the interest rate has a
negative effect on output but a positive effect on the price level. This
confirms the structuralists’ hypothesis that an increase in the interest rate
increases the working cost of capital and thus represents an adverse supply
shock rather than adverse demand shock.
In conclusion, these findings imply that policy makers in Pakistan
should be very careful when considering a revaluation of the currency or
using policy tools under their control in such a way as to achieve
appreciation. Similarly, we can say that it is recommended for the
authorities to implement a flexible exchange rate system because an
overvalued currency is not only inflationary but also hinders economic
growth.
Effects of the Exchange Rate on Output and Price Level
75
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